from sklearn.datasets import make_regression
import pandas as pd
import numpy as np

import plotly.express as px
import plotly.graph_objects as go

from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score

X,y=make_regression(n_samples=100,n_features=2,n_informative=2,n_targets=1,noise=50)

df=pd.DataFrame({'feature1':X[:,0],'feature2':X[:,1],'target':y})

df.shape

(100, 3)

df.corr()

	feature1	feature2	target
feature1	1.000000	0.134161	0.252948
feature2	0.134161	1.000000	0.794147
target	0.252948	0.794147	1.000000

#model=sm.ols('target ~ feature1 + feature2 ',df).fit()
#print(model.params)

fig=px.scatter_3d(df,x='feature1',y='feature2',z='target')
fig.show()

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=3)

from sklearn.linear_model import LinearRegression

lr=LinearRegression()
lr.fit(X_train,y_train)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
         normalize=False)

y_pred=lr.predict(X_test)

print("MAE",mean_absolute_error(y_test,y_pred))
print("MSE",mean_squared_error(y_test,y_pred))
print("R2 score ",r2_score(y_test,y_pred))

MAE 32.787670062326164
MSE 1886.2174902779639
R2 score  0.6359537944862013

x=np.linspace(-5,5,10)
y=np.linspace(-5,5,10)
xGrid,yGrid=np.meshgrid(y,x)
final=np.vstack((xGrid.ravel().reshape(1,100),yGrid.ravel().reshape(1,100))).T
z_final=lr.predict(final).reshape(10,10)
z=z_final

fig=px.scatter_3d(df,x='feature1',y='feature2',z='target')
fig.add_trace(go.Surface(x=x,y=y,z=z))
fig.show()

lr.coef_

array([11.13053336, 61.97800753])

lr.intercept_

-7.76793285104767